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The geography of Brexit – What geography? Modelling and predicting the outcome across 380 local authorities

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  • David Manley
  • Kelvyn Jones
  • Ron Johnston

Abstract

Most of the analysis before the 2016 referendum on the UK’s continued membership of the European Union based on opinion polling data focused on which groups were more likely to support each of the two options, with less attention to the geography of that support – although some regions, especially London and Scotland, were expected to provide substantial support for Remain. Using a recently developed procedure for detailed exploration of large tables derived from survey data, this paper presents the result of a prediction of the outcome across local authorities in Great Britain using just two variables – age and qualifications. In relative terms, that prediction was reasonably accurate – although, reflecting the polls’ overestimate of support for Remain it underestimated the number of places where Leave gained a majority, as was also the case within local authorities where data were published by ward. The model’s predictive value was enhanced by post hoc incorporation of information on turnout and the number of registered electors, and taking these into account there was little evidence of substantial, additional regional variation in levels of support for Leave. Overall, regions were relatively unimportant as influences on the referendum outcome once the characteristics of the people living there were taken into account.

Suggested Citation

  • David Manley & Kelvyn Jones & Ron Johnston, 2017. "The geography of Brexit – What geography? Modelling and predicting the outcome across 380 local authorities," Local Economy, London South Bank University, vol. 32(3), pages 183-203, May.
  • Handle: RePEc:sae:loceco:v:32:y:2017:i:3:p:183-203
    DOI: 10.1177/0269094217705248
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    References listed on IDEAS

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    1. Twigg, Liz & Moon, Graham, 2002. "Predicting small area health-related behaviour: a comparison of multilevel synthetic estimation and local survey data," Social Science & Medicine, Elsevier, vol. 54(6), pages 931-937, March.
    2. Twigg, Liz & Moon, Graham & Jones, Kelvyn, 2000. "Predicting small-area health-related behaviour: a comparison of smoking and drinking indicators," Social Science & Medicine, Elsevier, vol. 50(7-8), pages 1109-1120, April.
    3. Bart Los & Philip McCann & John Springford & Mark Thissen, 2017. "The mismatch between local voting and the local economic consequences of Brexit," Regional Studies, Taylor & Francis Journals, vol. 51(5), pages 786-799, May.
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    Cited by:

    1. Alexander Nurse & Olivier Sykes, 2019. "It’s more complicated than that!: Unpacking ‘Left Behind Britain’ and some other spatial tropes following the UK’s 2016 EU referendum," Local Economy, London South Bank University, vol. 34(6), pages 589-606, September.
    2. Simon Rudkin & Lucy Barros & Paweł Dłotko & Wanling Qiu, 2024. "An economic topology of the Brexit vote," Regional Studies, Taylor & Francis Journals, vol. 58(3), pages 601-618, March.
    3. Riccardo Crescenzi & Marco Di Cataldo & Alessandra Faggian, 2018. "Internationalized at work and localistic at home: The ‘split’ Europeanization behind Brexit," Papers in Regional Science, Wiley Blackwell, vol. 97(1), pages 117-132, March.
    4. Diana Gutiérrez‐Posada & María Plotnikova & Fernando Rubiera‐Morollón, 2021. "“The grass is greener on the other side”: The relationship between the Brexit referendum results and spatial inequalities at the local level," Papers in Regional Science, Wiley Blackwell, vol. 100(6), pages 1481-1500, December.
    5. Moon, Graham & Twigg, Liz & Jones, Kelvyn & Aitken, Grant & Taylor, Joanna, 2019. "The utility of geodemographic indicators in small area estimates of limiting long-term illness," Social Science & Medicine, Elsevier, vol. 227(C), pages 47-55.

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